Beyond genealogies: Mutual information of causal paths to analyse single cell 
tracking data

Scherf, Nico; Zerjatke, Thomas; Klemm, Konstantin; Glauche, Ingmar; Roeder, Ingo

doi:10.1109/ISBI.2013.6556506

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Conference Paper

Beyond genealogies: Mutual information of causal paths to analyse single cell tracking data

MPS-Authors

There are no MPG-Authors in the publication available

External Resource

No external resources are shared

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

There are no public fulltexts stored in PuRe

Supplementary Material (public)

There is no public supplementary material available

Citation

Scherf, N., Zerjatke, T., Klemm, K., Glauche, I., & Roeder, I. (2013). Beyond genealogies: Mutual information of causal paths to analyse single cell tracking data. In Proceedings of the 10th International Symposium on Biomedical Imaging (pp. 440-443). doi:10.1109/ISBI.2013.6556506.

Cite as: https://hdl.handle.net/21.11116/0000-0007-D256-B

Abstract

Single cell tracking, based on the computerised analysis of time-lapse movies, is a sophisticated experimental technique to quantify single cell dynamics in time and space. Although the resulting cellular genealogies comprehensively describe the divisional history of each cell, there are many open questions regarding the statistical analysis of this type of data. In particular, it is unclear, how tracking uncertainties or spatial information of cellular development can correctly be incorporated into the analysis. Here we propose a generalised description of single cell tracking data by spatiotemporal networks that can account for ambiguities in cell assignment as well as for spatial relations between cells. We present a way to measure correlations among cell states by analysing the mutual information in state space considering causal (time-respecting) paths and illustrate our approach by a corresponding example. We conclude that a comprehensive spatiotemporal description of single cell tracking data is ultimately necessary to fully exploit the information obtained by time-lapse imaging.